An Intercomparison of SMAP Passive Soil Moisture Retrieval Algorithms

Launched in January 2015, the Soil Moisture Active Passive (SMAP) observatory uses an active instrument (radar) and a passive instrument (radiometer) to achieve high-resolution mapping of soil moisture and detection of freeze/thaw state. Other than a few scheduled down times due to hardware diagnostics and spacecraft maneuvers, data acquisition by the radiometer has been essentially uninterrupted since March 2015, resulting in a trove of well-calibrated L-band observations for retrieval of soil moisture in the top 5 cm of soil. Since the start of the data acquisition the passive soil moisture algorithm team and Cal/Val team have been working together to evaluate the performance of the Level 2 passive soil moisture product (L2_SM_P) using a variety of in situ data from core validation sites and sparse networks via a partnership with international data providers. To date, the Cal/Val activities enabled by this partnership have led to beneficial algorithm enhancements and improvements in the passive soil moisture product.

In this presentation, we will describe the soil moisture retrieval algorithms currently coded in the L2_SM_P science production software. Among these algorithms are the current baseline H-pol single channel algorithm (SCA-H), the V-pol single channel algorithm (SCA-V), the dual channel algorithm (DCA), and the microwave polarization ratio algorithm (MPRA). Results of performance using these algorithms will be presented and discussed in terms of common metrics (e.g. ubRMSE, bias, and correlation) based on comparison of the retrieved soil moisture with in situ soil moisture from core validation sites and sparse networks. The strengths and shortcomings of individual algorithms will be discussed and a path for further refinements of SMAP passive soil moisture retrieval algorithms will be suggested.